Modeling behavioral change and COVID-19 containment in Mexico: A trade-off between lockdown and compliance

• Published in: Mathematical biosciences Vol. 325; p. 108370
• Main Authors: Acuña-Zegarra, Manuel Adrian, Santana-Cibrian, Mario, Velasco-Hernandez, Jorge X.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2020; Elsevier Science Ltd
• Subjects: Abandonment; Bayesian inference; Behavioral change; Communicable Disease Control; Computer simulation; Contact rate reduction; Containment; Coronavirus Infections - prevention & control; Coronaviruses; COVID-19; Disease control; Disease dispersal; Disease spread; Disease transmission; Epidemics; Health Behavior; Humans; Isolation; Mathematical models; Mexico; Models, Theoretical; Mortality; Pandemics - prevention & control; Pneumonia, Viral - prevention & control; Population; Population studies; Risk Reduction Behavior; Signs and symptoms; Social Isolation; Tradeoffs; Viral diseases; Mexico; COVID-19; Isolation; Behavioral change; Bayesian inference; Contact rate reduction; Disease dispersal
• ISSN: 0025-5564; 1879-3134; 1879-3134
• DOI: 10.1016/j.mbs.2020.108370

Sanitary Emergency Measures (SEM) were implemented in Mexico on March 30th, 2020 requiring the suspension of non-essential activities. This action followed a Healthy Distance Sanitary action on March 23rd, 2020. The aim of both measures was to reduce community transmission of COVID-19 in Mexico by lowering the effective contact rate. Using a modification of the Kermack–McKendrick SEIR model we explore the effect of behavioral changes required to lower community transmission by introducing a time-varying contact rate, and the consequences of disease spread in a population subject to suspension of non-essential activities. Our study shows that there exists a trade-off between the proportion of the population under SEM and the average time an individual is committed to all the behavioral changes needed to achieve an effective social distancing. This trade-off generates an optimum value for the proportion of the population under strict mitigation measures, significantly below 1 in some cases, that minimizes maximum COVID-19 incidence. We study the population-level impact of three key factors: the implementation of behavior change control measures, the time horizon necessary to reduce the effective contact rate and the proportion of people under SEM in combating COVID-19. Our model is fitted to the available data. The initial phase of the epidemic, from February 17th to March 23rd, 2020, is used to estimate the contact rates, infectious periods and mortality rate using both confirmed cases (by date of symptoms initiation), and daily mortality. Data on deaths after March 23rd, 2020 is used to estimate the mortality rate after the mitigation measures are implemented. Our simulations indicate that the most likely dates for maximum incidence are between late May and early June, 2020 under a scenario of high SEM compliance and low SEM abandonment rate. •A CoVID-19 model is presented considering suspension of non-essential activities.•The model explores the effect of behavioral changes required to reduce transmission.•Key parameters of the model are estimated using Bayesian inference.•There is an optimum value of the lockdown proportion which minimizes incidence.•Our scenarios show that incidence peak date occurs later than government estimates.